Tail Inventory Centralization: A Global Strategy for Cost Efficiency

In today’s global fulfillment environment, most logistics organizations focus their optimization efforts on fast-moving, high-volume SKUs. This makes sense: these products generate the lion’s share of customer orders, and they account for most throughput and sales velocity. However, in my experience leading strategic supply chain initiatives at Amazon, I’ve seen that the true opportunity to reduce fulfillment cost and simplify complexity often lies in the long tail — the bottom 20% of SKUs with low demand and high handling cost.

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Tail inventory may not generate high sales, but it consumes significant resources. These slow movers often get over-distributed across multiple fulfillment centers out of an abundance of caution — a practice that seems harmless but has serious cost consequences. At Amazon, we noticed that many tail SKUs were stocked in more than five regional nodes despite having low sales velocity. As a result, they traveled an average of 716 miles per package — nearly double the mileage of head SKUs. This meant higher cost per package, more long-haul shipments, and frequent stockouts in core regions.

To address this challenge, we segmented our entire SKU portfolio using a four-tier demand framework: Head (Top 55%), Body (20–55%), Tail (5–20%), and Deep Tail (Bottom 5%). Each segment had different handling, replenishment, and stocking profiles. Our strategy focused on Tail and Deep Tail SKUs, which had sporadic demand, long replenishment lead times, and a high cost-to-serve. The insight was clear: instead of spreading these SKUs thinly across the network, we could consolidate them in fewer, strategically located fulfillment centers.

We developed a centralization model based on several factors: historical order dispersion, forecast variability, storage cost, node proximity, and network throughput. The goal was to minimize long-haul shipments while maintaining service levels. We used a combination of demand clustering and replenishment simulation to identify ideal stocking locations. For example, rather than stocking a low-velocity SKU in five U.S. regions, we could fulfill it from two central nodes that covered 90% of its national demand footprint.

The results were transformative. Average fulfillment miles dropped by approximately 40% for Tail SKUs. In-region in-stock (IRIS) and in-region assignment (IRA) metrics — two key indicators of inventory efficiency — improved by 20–30%. The modeled cost opportunity exceeded $329 million across just one non-sortable fulfillment segment. This wasn’t just a cost optimization play; it improved slotting efficiency, reduced cross-regional shipments, and lowered inbound freight congestion at our busiest fulfillment centers.

Of course, this wasn’t without its challenges. Centralizing inventory meant revisiting node-level capacity constraints, re-aligning safety stock logic, and working with inbound planning teams to adjust replenishment flows. We had to develop rule-based logic to determine what SKUs qualified for centralization and where they could be safely removed without impacting customer experience. Communication and alignment across cross-functional teams — supply planning, inventory placement, transportation, and fulfillment — was critical.

One area where we saw huge value was in slot utilization. Tail SKUs, when sparsely demanded and highly scattered, often occupy prime bin space in regional FCs that could be better used by faster-moving products. By removing these SKUs from multiple regions and concentrating them in a central site with specialized handling zones, we not only saved space but improved pick-path efficiency and outbound dock flow.

Another downstream benefit was better inbound flow predictability. Central nodes saw a more consistent SKU profile, which allowed for smoother vendor scheduling and inbound slot management. This further reduced overall cost-to-serve while improving vendor experience. The fewer the nodes receiving low-velocity SKUs, the better we could manage labor, trailer space, and congestion.

This strategy also aligned well with our broader regionalization effort — a $600M initiative that shifted Amazon’s network from a national to regional fulfillment model. Tail SKU centralization played a key role in allowing regional FCs to focus on high-volume throughput, while specialized sites handled exception items and slow movers efficiently. It was a win-win for both cost and customer experience.

What’s the broader takeaway for supply chain leaders? Tail inventory needs a different playbook. Don’t treat all SKUs equally. Segment by demand profile, simulate travel paths, and test consolidation strategies. If you’re managing a network with 3 or more nodes, chances are you’re overstocking and over-shipping low-velocity SKUs without realizing it.

Here’s a quick framework you can adapt to your own network:

1. Segment SKUs using demand tiers (Head, Body, Tail, Deep Tail)

2. Analyze average travel distance per SKU tier

3. Identify SKUs with high travel cost but low sales density

4. Model central stocking nodes based on national/regional coverage

5. Pilot the strategy on a small subset, then scale it up

As e-commerce continues to scale and fulfillment expectations rise, we can’t afford to treat tail inventory as an afterthought. Centralizing low-demand items is a network-level efficiency lever that improves cost, simplifies operations, and reduces carbon footprint. More importantly, it frees up high-velocity capacity in your core regions — giving your top sellers the space and attention they deserve.

If you want to improve fulfillment efficiency, reduce transportation cost, and improve warehouse throughput, the answer might lie in your slowest-moving products. Treat your tail like a head — and the entire body of your supply chain will benefit.

Figure 1: Relative Cost Index by SKU Tier

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